1. Field of the Invention
The present invention relates to the field of speech processing, and, more particularly, to dynamically adjusting speech grammar weights based on usage statistics.
2. Description of the Related Art
A speech recognition grammar defines valid words or phrases that are to be speech recognized. A speech recognition engine compares the words and phrases against an utterance and determines a probability that the utterance matches one or more of the words in the grammar. Speech recognition grammar weights can be associated with grammar words, where each weight represents a probability or likelihood that an associated word will be spoken. A grammar word having a greater relative weight than other grammar words is more likely to be matched to an utterance.
Grammar weights can be effectively used to tactically increasing speech recognition accuracy. That is, weights allow for performance improvements without degrading overall speech recognition accuracy outside a context of a particular application or grammar.
Additionally, the use of grammar weights is a relatively light-weight technique that does not add significant computational overhead to a speech recognition system. Accordingly, grammar weights can be an effective technique to customize performance of a speech recognition system. This is especially true for embedded speech recognition systems that can have severe resource constraints, such as speech recognition systems often found in mobile phones or other consumer electronic devices.
Conventional techniques for applying grammar weights are primarily static processes optimized for a theoretical average user. In other words, grammar weights are adjusted so that an estimated most utilized grammar word will have a higher weight than a lesser utilized word. These optimizations for an average user, however, do not fit the usage patterns for all users, but instead represent a compromise established for a generic user.
No known conventional technology automatically and dynamically adjust grammar weights based upon actual usage of a speech recognition system by a user or set of users. These usages can be significantly different from the theoretical usage estimates for which factory established grammar weights are optimized. In conventional systems, speech recognition accuracy increasingly degrades as the actual usages of a speech-enabled system diverge from the theoretical usage estimates.